Epilepsy Electroencephalogram Research Articles

Automatic epilepsy detection with an attention-based multiscale residual network

The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.

MAC: EPILEPSY EEG SIGNAL RECOGNITION BASED ON THE MLP-SELF-ATTENTION MODEL AND COSINE DISTANCE

In current epilepsy disease research, accurate identification of epilepsy electroencephalogram (EEG) signals is crucial for improving diagnostic efficiency and developing personalized treatment plans. This study proposes an innovative epilepsy recognition model, MAC, which combines the unique advantages of a multilayer perceptron (MLP), a self-attention mechanism and the cosine distance. This model uses a MLP as the basic model and effectively reduces individual differences among epilepsy patients through its superior linear fitting ability. To more accurately measure the difference between two EEG signals, we introduced the cosine distance as a new feature metric. This metric enhances the performance of epilepsy EEG classification by using the cosine value of the angle in vector space to precisely assess the difference between two individuals. In addition, we introduced a self-attention mechanism into the model to enhance the impact of various factors on the final EEG data. Our experiments employed the EEG database of the Epilepsy Research Center of the University of Bonn. Through comparative experiments, it was proven that the proposed MAC model achieved significant improvement in performance on the epilepsy EEG signal recognition task. This study fills the existing research gap in the field of epilepsy identification and provides a powerful tool for the accurate diagnosis of epilepsy diseases in the future. We believe that the introduction of the MAC model will promote new breakthroughs in epilepsy EEG signal recognition and lay a solid foundation for the development of related fields. This research provides an important theoretical and practical reference for advancing the field of epilepsy identification.

Positional multi-length and mutual-attention network for epileptic seizure classification

The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods.

Computerized Multidomain EEG Classification System: A New Paradigm.

The recent advancements in electroencepha- logram (EEG) signals classification largely center around the domain-specific solutions that hinder the algorithm cross-discipline adaptability. This study introduces a computer-aided broad learning EEG system (CABLES) for the classification of six distinct EEG domains under a unified sequential framework. Specifically, this paper proposes three novel modules namely, complex variational mode de- composition (CVMD), ensemble optimization-based featu- res selection (EOFS), and t-distributed stochastic neighbor embedding-based samples reduction (tSNE-SR) methods respectively for the realization of CABLES. Extensive expe- riments are carried out on seven different datasets from diverse disciplines using different variants of the neural network, extreme learning machine, and machine learning classifiers employing a 10-fold cross-validation strategy. Results compared with existing studies reveal that the highest classification accuracy of 99.1%, 97.8%, 94.3%, 91.5%, 98.9%, 95.3%, and 92% is achieved for the motor imagery dataset A, dataset B, slow cortical potentials, epilepsy, alcoholic, and schizophrenia EEG datasets res- pectively. The overall empirical analysis authenticates that the proposed CABLES framework outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus can be endorsed as an effective automated neural rehabilitation system.

Synchroextracting chirplet transform-based epileptic seizures detection using EEG

With the development of computer technology, computer-aided medical diagnosis based on signal processing is trending up. In recent years, the detection of epilepsy electroencephalogram (EEG) signals for clinical application has attracted extensive attention from many researchers. In this paper, a novel epilepsy classification model is proposed based on the time-frequency (TF) analysis method named synchroextracting chirplet transform (SECT). The energy concentrated TF representation of the signal is first obtained benefit by the introduced parameter chirp rate. Then the instantaneous frequency (IF) trajectory is estimated by extracting the TF coefficients most relevant to the signal through the synchroextracting chirplet operator (SECO). The original signal can be simple and effectively reconstructed using the accurate IF trajectory with anti-noise interference ability. Singular value decomposition is performed on the TF matrix to obtain the feature vectors with strong characterization capacity for different epilepsy behaviors, and the multiple classification tasks are finally completed with support vector machine (SVM) classifier. Two authoritative epilepsy datasets are employed in our work to verify the effectiveness of the proposed scheme. No less than 99.17 % accuracy, 99.33 % specificity, and 99.28 % sensitivity are achieved on a total of ten classification tasks that are closely related to the actual clinical applications in the Bonn dataset; an average of 99.29 % accuracy and 0.97 matthews correlation coefficient (MCC) are obtained among all subjects in the long-term EEG database CHB-MIT. The noise robustness and portability of our proposal are verified through the stable experimental results. The superior detection accuracy also provides clinicians with auxiliary diagnosis.

Novel multi-view Takagi–Sugeno–Kang fuzzy system for epilepsy EEG detection

Most intelligent algorithms used for recognizing epilepsy electroencephalogram (EEG) have two major deficiencies. The one is the lack of interpretability and the other is unsatisfactory recognition results. In response to these challenges, we propose a dedicated model called multi-view Takagi–Sugeno–Kang (TSK) fuzzy system (MV-TSK-FS) for the epilepsy EEG detection. Our contributions lie in three aspects. First, TSK-FS is selected as the basic model. As one of the most famous fuzzy systems, TSK-FS has the advantage of nice interpretability and thus meets the requirement of clinic trials and applications. Second, MV-TSK-FS uses a multi-view framework to collaboratively handle the collective feature data extracted from diverse extraction perspectives, which strives to avoid the potential performance degradation commonly incurred with single feature extraction. Third, we propose a view-weighted mechanism based on the quadratic regularization to distinguish the importance of each view. The more important the view, the larger the corresponding weight is. The final decision is consequently figured out with the weighted outputs of all views. Experimental results demonstrate that, compared with other epilepsy EEG detection ones, our proposed method has better classification performance as well as more satisfied interpretability on results.

Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification

At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.

Cognitive Dysfunction in Juvenile Myoclonic Epilepsy (JME) - A Tertiary Care Center Study.

Background and Aim:Epilepsy often leads to cognitive impairment. Idiopathic generalized epilepsy as a group is considered to be benign in terms of its effects on cognition. Though, neuropsychological testing reveals subtle frontal impairment in patients with juvenile myoclonic epilepsy (JME). The aim of this study is to evaluate cognitive dysfunction in patients with JME.Method:We compared 50 JME patients and 50 age and sex matched healthy controls above 12 years of age on various cognitive tests which included Mini Mental State Examination (MMSE), Frontal Assessment Battery (FAB), Executive Interview (EXIT), PGI Memory Scale (PGIMS), Clock Drawing Test (CDT), Cube copying test (CCT), and Nahor Benson Test (NBT). We correlated the cognitive dysfunction with education level, age of onset, duration of epilepsy, electroencephalogram (EEG) abnormalities, treatment, and seizure control status.Results:JME patients performed significantly worse on MMSE (P = 0.001), PGI MS (P value = 0.001), FAB (P =.001), EXIT (P =.001), CDT (P =.02), and CCT (P =.001) when compared to the controls. JME patients had impaired attention, verbal fluency, design fluency, verbal memory, visual memory, conceptualization, set shifting, mental flexibility, response inhibition, and visuospatial functions. Cognitive dysfunction correlated with education level, duration of epilepsy and EEG abnormality. No correlation was seen with seizure frequency or type of antiepileptic therapy.Conclusions:JME patients demonstrate both frontal and parietooccipital lobe dysfunction. Hence detailed higher mental function tests supplemented by functional neuroimaging studies should be done in JME patients for their comprehensive management. This would also enhance our knowledge about the pathogenesis of JME.

Automatic epileptic EEG classification based on differential entropy and attention model

In epilepsy electroencephalogram (EEG) analysis, clinicians usually interpret EEG page by page, which is time-consuming and brings heavy workload. This paper proposes a novel automatic epileptic EEG classification approach based on differential entropy and attention mechanism, aiming at designing a short-term epileptic EEG classification model with high accuracy and good generalization performance. Firstly, the original EEG recordings are decomposed into five sub-frequency bands which approximately obey the Gaussian distribution. Afterward, a improved attention model framework considering both row and column attention with a shallower VGGNet (AttVGGNet-RC) is put forward as the classifier. Finally, non-patient specific method is employed to evaluate the performance with pre-tuned hypermeters. With 8-fold data, the proposed model yielded 77.33 ± 2.91% sensitivity, 86.67 ± 3.70% specificity and 82.00 ± 1.43% accuracy, and accuracy was increased by 5.34%, 8.99%, 26.24% and 4.47% respectively compared with multi-layer perceptron (MLP), extreme learning machine (ELM), support vector machine (SVM) and Long Short-Term Memory (LSTM). With 10-fold shuffled data, the improved attention model yielded 93.84 ± 0.63% sensitivity, 95.84 ± 0.74% specificity and 95.12 ± 0.20% accuracy, and the accuracy was 1.34%, 16.29%, 27.12% and 8.24% higher than MLP, ELM, SVM and LSTM respectively. The experimental result showed that the attention model achieved high classification accuracy with low standard deviation as well as good generalization performance. Furthermore, compared with state-of-art epilepsy analysis system, the proposed approach also show better performance. Therefore, this study has significant clinical application value in epilepsy analysis.

Classification of EEG signals in epilepsy using a novel integrated TSK fuzzy system

The use of machine learning technology to recognize electrical signals of the brain is becoming increasingly popular. Compared with doctors’ manual judgment, machine learning methods are faster. However, only when its recognition accuracy reaches a high level can it be used in practice. Due to the difference in the data distributions of the training dataset and the test dataset and the lack of training samples, the classification accuracies of general machine learning algorithms are not satisfactory. In fact, among the many machine learning methods used to process epilepsy electroencephalogram (EEG) signals, most are black box methods; however, in medicine, methods with explanatory power are needed. In response to these three challenges, this paper proposes a novel technique based on domain adaptation learning, semi-supervised learning and a fuzzy system. In detail, we use domain adaptation learning to reduce deviation from the data distribution, semi-supervised learning to compensate for the lack of training samples, and the Takagi-Sugen-Kang (TSK) fuzzy system model to improve interpretability. Our experimental results show that the performance of the new method is better than those of most advanced epilepsy classification methods.

EEG Signal Research for Identification of Epilepsy using Machine Learning Classification Accession

Epilepsy is censorious neurological disorder in which nerve cell activity in the brain is disturbed causing recurrent seizures which are sudden, uncontrolled electrical discharges in the brain cell. In clinical treatment of epileptic patients seizure reorganization has much prominence. Hence in detecting the phenomenon of epilepsy Electroencephalogram (EEG) signal is widely used as it includes important carnal data of the brain. Though it is critical to analyze the EEG signal and identify the seizures. So feature extraction of EEG signal plays a vital role for epilepsy detection. This paper describes an worthwhile feature extraction based on variational mode decomposition (VMD) to identify epilepsy. The extracted features fed to ANN, KNN and SVM in order to classify epilepsy. The performance of the SVM classifier shows the better classification compared to existing methods.

A Novel Double-Index-Constrained, Multi-View, Fuzzy-Clustering Algorithm and its Application for Detecting Epilepsy Electroencephalogram Signals

When processing a multi-view, epilepsy electroencephalogram (EEG) dataset, the traditional single-view clustering algorithms cannot fully mine the correlation information between each view and identify the importance of each view because of the limitations of its own methods. This limitation causes poor clustering performance when using these classic, single-view clustering algorithms. To solve this problem, a novel double-index-constrained, multi-view, fuzzy clustering algorithm (DIC-MV-FCM) is proposed for the automatic detection of epilepsy EEG data. The DIC-MV-FCM algorithm is integrated into the multi-view clustering technology and the view-weighted adaptive learning strategy, which can effectively use the correlation information between each view and control the importance of each view to improve the final clustering performance. The experimental results using several epilepsy EEG datasets show that the proposed DIC-MV-FCM algorithm has better clustering performance than the traditional clustering algorithms for processing multi-view EEG data.

Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike's Information Criterion

The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike's information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.

Parameter pattern discovery in nonlinear dynamic model for EEGs analysis.

We propose a nonlinear dynamic model for an invasive electroencephalogram analysis that learns the optimal parameters of the neural population model via the Levenberg-Marquardt algorithm. We introduce the crucial windows where the estimated parameters present patterns before seizure onset. The optimal parameters minimizes the error between the observed signal and the generated signal by the model. The proposed approach effectively discriminates between healthy signals and epileptic seizure signals. We evaluate the proposed method using an electroencephalogram dataset with normal and epileptic seizure sequences. The empirical results show that the patterns of parameters as a seizure approach and the method is efficient in analyzing nonlinear epilepsy electroencephalogram data. The accuracy of estimating the optimal parameters is improved by using the nonlinear dynamic model.

Hidden discriminative features extraction for supervised high-order time series modeling

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice.

Detrended Cross-Correlation Analysis of Epilepsy Electroencephalagram Signals

The cross-correlation performance between epilepsy electroencephalogram (EEG) signals reflects the status of epilepsy patients which has importance for analyzing long-range correlation of non-stationary signals. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different physiological and pathological states of epilepsy EEG signals. It were compared the difference of DCCA values between epilepsy patients EEG signals and normal subjects EEG signals. It was found that the DCCA values of epilepsy patients EEG signals increased compared the normal subjects EEG signals which can be helpful for medical diagnosis and treatment.

The Electro Encephalogram in Epilepsy and in Conditions of Impaired Consciousness

(1968). The Electro Encephalogram in Epilepsy and in Conditions of Impaired Consciousness. American Journal of EEG Technology: Vol. 8, No. 2, pp. 59-73.

Some Clinical Aspects of the Normal Electroencephalogram in Epilepsy

WHAT does it mean if the electroencephalogram of an epileptic patient is normal between seizures? On the one hand, the physician may have been led to believe that an abnormal electroencephalogram is highly characteristic of epilepsy. On the other hand, if he regularly refers his epileptic patients for electroencephalographic study, he forms the impression that, for a goodly portion of them, the tracings, at least at the time of examination, which is almost always done between seizures, are described as normal. This may be true even for patients examined when not taking anticonvulsive medicine. Statistically, a review of the literature . . .

Aspects of the electroencephalogram in epilepsy and feeblemindedness

In 27 unselected feebleminded cases a negative correlation (−.54±.09) was obtained between the per cent time alpha of the electroencephalogram and chronological age. Distribution of per cent time alpha was definitely skewed toward the lower per cent time alpha categories.

The Electro-encephalogram in Epilepsy and in Conditions of Impaired Consciousness. (Arch. Neur. and Psychiat., vol. xxxiv, p. 1133, Dec., 1935.) Gibbs, F. A., et al.

<i>The Electro-encephalogram in Epilepsy and in Conditions of Impaired Consciousness.</i> (<i>Arch. Neur. and Psychiat., vol. xxxiv, p.</i> 1133, <i>Dec.</i>, 1935.) <i>Gibbs, F. A., et al.</i>